ADT: A Generalized Algorithm and Program for Beyond Born–Oppenheimer Equations of “<i>N</i>” Dimensional Sub-Hilbert Space by Koushik Naskar (7510592)

“…In order to overcome such shortcoming, we develop a generalized algorithm, “ADT” to generate the nonadiabatic equations through symbolic manipulation and to construct highly accurate diabatic surfaces for molecular processes involving excited electronic states. …”

Comparison of regression fits of annual growth rate (%) as a function of the D5+/D20+ ratio (solid line) and P index (dashed line). by Patrik Galeta (16015459)

The repair algorithm restores the electrophysiological behaviour of cut and repaired mouse pyramidal cells and allows for better predictions of neuronal function in human neurons. by Moritz Groden (18018276)

Differences in computing performance between ENQUIRE’s gene normalization algorithm and GNorm2-Bioformer. by Luca Musella (19986312)

Definitions of global parameters used for constructing random ecosystems. by Robert Marsland (8616483)

Presentation1_A Photovoltaic Power Predicting Model Using the Differential Evolution Algorithm and Multi-Task Learning.pdf by Senshen Pang (13025715)

BOFdat Step 3: Identifying species-specific metabolic end goals. by Jean-Christophe Lachance (6619307)

“…A simplified metabolic network composed of two linear pathways is depicted with its corresponding stoichiometric matrix S, in which the objective function is presented in the blue column (<i>v</i>_<i>obj</i>). …”

Algorithm for extending the audiogram with loudness growth. by Lars Lambriks (14591615)

Fit to Patient’s 1 data under a constant variance assumption. by Patricio Cumsille (6755882)

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Fit to Patient’s 2 data under a constant variance assumption. by Patricio Cumsille (6755882)

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Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic by NELSON L. DIAS (8078003)

“…These implementations need to utilize high-precision arithmetic, and are programmed in the Python programming language. A thorough comparison with existing R functions allows the identification of cases when the latter fail, and provide further guidance to their use.…”

Fit to Patient’s 2 data under a non-constant variance assumption. by Patricio Cumsille (6755882)

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Fit to Patient’s 1 data under a non-constant variance assumption. by Patricio Cumsille (6755882)

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PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

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Statistics for the goodness of fit to Patient’s 2 data under constant a variance assumption. by Patricio Cumsille (6755882)

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Estimated model parameters for Patient’s 2 data under a constant variance assumption. by Patricio Cumsille (6755882)

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Estimated model parameters for Patient’s 1 data under a constant variance assumption. by Patricio Cumsille (6755882)

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Final path. by Zhenggang Wang (1753657)

Subjects: “…target gravitational function…”

Expansion procedure. by Zhenggang Wang (1753657)

Subjects: “…target gravitational function…”

Artificial potential field method. by Zhenggang Wang (1753657)

Subjects: “…target gravitational function…”